



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase						: Optional[Any]     						=logging.get_logger(__name__)

def       lowerCAmelCase_	(    _lowercase							:  str)				->			str:







			"""simple docstring"""
			a__  :		Tuple					=    SwinConfig.from_pretrained(
			    """microsoft/swin-tiny-patch4-window7-224"""		,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""])
			a__  :		Optional[Any]					=    MaskFormerConfig(backbone_config=lowerCAmelCase__)

			a__  :		Union[str, Any]					=    """huggingface/label-files"""
			if "ade20k-full" in model_name:
						# this should be ok
						a__  :		Any					=    847
						a__  :		List[Any]					=    """maskformer-ade20k-full-id2label.json"""
			elif "ade" in model_name:
						# this should be ok
						a__  :		str					=    150
						a__  :		List[Any]					=    """ade20k-id2label.json"""
			elif "coco-stuff" in model_name:
						# this should be ok
						a__  :		str					=    171
						a__  :		str					=    """maskformer-coco-stuff-id2label.json"""
			elif "coco" in model_name:
						# TODO
						a__  :		List[str]					=    133
						a__  :		Any					=    """coco-panoptic-id2label.json"""
			elif "cityscapes" in model_name:
						# this should be ok
						a__  :		Optional[int]					=    19
						a__  :		Dict					=    """cityscapes-id2label.json"""
			elif "vistas" in model_name:
						# this should be ok
						a__  :		Dict					=    65
						a__  :		Any					=    """mapillary-vistas-id2label.json"""

			a__  :		int					=    json.load(open(hf_hub_download(lowerCAmelCase__		,  lowerCAmelCase__		,  repo_type="""dataset""")		,  """r"""))
			a__  :		Optional[int]					=    {int(lowerCAmelCase__): v for k, v in idalabel.items()}

			return config

def       lowerCAmelCase_	(    _lowercase							:  Any)				->			Any:







			"""simple docstring"""
			a__  :		str					=    []
			# stem
			# fmt: off
			rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight"""))
			rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias"""))
			rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight"""))
			rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias"""))
			# stages
			for i in range(len(config.backbone_config.depths)):
						for j in range(config.backbone_config.depths[i]):
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight'''))
									rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias'''))

						if i < 3:
									rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight'''))
									rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight'''))
									rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias'''))
						rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight'''))
						rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias'''))

			# FPN
			rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight"""))
			rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight"""))
			rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias"""))
			for source_index, target_index in zip(range(3		,  0		,  -1)		,  range(0		,  3)):
						rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight'''))
						rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight'''))
						rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias'''))
						rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight'''))
						rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight'''))
						rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias'''))
			rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight"""))
			rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias"""))

			# Transformer decoder
			for idx in range(config.decoder_config.decoder_layers):
						# self-attention out projection
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias'''))
						# cross-attention out projection
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias'''))
						# MLP 1
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias'''))
						# MLP 2
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias'''))
						# layernorm 1 (self-attention layernorm)
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias'''))
						# layernorm 2 (cross-attention layernorm)
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias'''))
						# layernorm 3 (final layernorm)
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias'''))

			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight"""))
			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias"""))

			# heads on top
			rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight"""))

			rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight"""))
			rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias"""))

			rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight"""))
			rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias"""))

			for i in range(3):
						rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight'''))
						rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias'''))
			# fmt: on

			return rename_keys

def       lowerCAmelCase_	(    _lowercase							:  List[Any]		,  _lowercase							:  str		,  _lowercase							:  Union[str, Any])				->			Tuple:







			"""simple docstring"""
			a__  :		List[Any]					=    dct.pop(lowerCAmelCase__)
			a__  :		Dict					=    val

def       lowerCAmelCase_	(    _lowercase							:  str		,  _lowercase							:  List[str])				->			Optional[int]:







			"""simple docstring"""
			a__  :		Union[str, Any]					=    [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
			for i in range(len(backbone_config.depths)):
						a__  :		Union[str, Any]					=    num_features[i]
						for j in range(backbone_config.depths[i]):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									a__  :		int					=    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight''')
									a__  :		Any					=    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias''')
									# next, add query, keys and values (in that order) to the state dict
									a__  :		Any					=    in_proj_weight[:dim, :]
									a__  :		List[Any]					=    in_proj_bias[: dim]
									a__  :		Any					=    in_proj_weight[
									    dim : dim * 2, :
									]
									a__  :		Optional[int]					=    in_proj_bias[
									    dim : dim * 2
									]
									a__  :		Dict					=    in_proj_weight[
									    -dim :, :
									]
									a__  :		int					=    in_proj_bias[-dim :]
									# fmt: on

def       lowerCAmelCase_	(    _lowercase							:  int		,  _lowercase							:  Optional[Any])				->			Optional[Any]:







			"""simple docstring"""
			a__  :		str					=    config.decoder_config.hidden_size
			for idx in range(config.decoder_config.decoder_layers):
						# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
						a__  :		Dict					=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight''')
						a__  :		List[Any]					=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias''')
						# next, add query, keys and values (in that order) to the state dict
						a__  :		Any					=    in_proj_weight[: hidden_size, :]
						a__  :		Any					=    in_proj_bias[:config.hidden_size]
						a__  :		Dict					=    in_proj_weight[hidden_size : hidden_size * 2, :]
						a__  :		Dict					=    in_proj_bias[hidden_size : hidden_size * 2]
						a__  :		int					=    in_proj_weight[-hidden_size :, :]
						a__  :		Any					=    in_proj_bias[-hidden_size :]
						# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
						a__  :		int					=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight''')
						a__  :		Any					=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias''')
						# next, add query, keys and values (in that order) to the state dict
						a__  :		str					=    in_proj_weight[: hidden_size, :]
						a__  :		Dict					=    in_proj_bias[:config.hidden_size]
						a__  :		Any					=    in_proj_weight[hidden_size : hidden_size * 2, :]
						a__  :		Any					=    in_proj_bias[hidden_size : hidden_size * 2]
						a__  :		Dict					=    in_proj_weight[-hidden_size :, :]
						a__  :		Dict					=    in_proj_bias[-hidden_size :]
			# fmt: on

def       lowerCAmelCase_	(    )				->			Tuple:







			"""simple docstring"""
			a__  :		Any					=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a__  :		Any					=    Image.open(requests.get(lowerCAmelCase__		,  stream=lowerCAmelCase__).raw)
			return im

@torch.no_grad()
def       lowerCAmelCase_	(    _lowercase							:  str		,  _lowercase							:  str		,  _lowercase							:  str		,  _lowercase							:  bool = False)				->			Tuple:







			"""simple docstring"""
			a__  :		Any					=    get_maskformer_config(lowerCAmelCase__)

			# load original state_dict
			with open(lowerCAmelCase__		,  """rb""") as f:
						a__  :		List[Any]					=    pickle.load(lowerCAmelCase__)
			a__  :		Optional[int]					=    data["""model"""]

			# for name, param in state_dict.items():
			#     print(name, param.shape)

			# rename keys
			a__  :		Any					=    create_rename_keys(lowerCAmelCase__)
			for src, dest in rename_keys:
						rename_key(lowerCAmelCase__		,  lowerCAmelCase__		,  lowerCAmelCase__)
			read_in_swin_q_k_v(lowerCAmelCase__		,  config.backbone_config)
			read_in_decoder_q_k_v(lowerCAmelCase__		,  lowerCAmelCase__)

			# update to torch tensors
			for key, value in state_dict.items():
						a__  :		Dict					=    torch.from_numpy(lowerCAmelCase__)

			# load 🤗 model
			a__  :		List[str]					=    MaskFormerForInstanceSegmentation(lowerCAmelCase__)
			model.eval()

			for name, param in model.named_parameters():
						print(lowerCAmelCase__		,  param.shape)

			a__  :		List[str]					=    model.load_state_dict(lowerCAmelCase__		,  strict=lowerCAmelCase__)
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(lowerCAmelCase__) == 0, F'''Unexpected keys: {unexpected_keys}'''

			# verify results
			a__  :		int					=    prepare_img()
			if "vistas" in model_name:
						a__  :		Optional[Any]					=    65
			elif "cityscapes" in model_name:
						a__  :		Dict					=    6_5535
			else:
						a__  :		str					=    255
			a__  :		Optional[int]					=    True if """ade""" in model_name else False
			a__  :		int					=    MaskFormerImageProcessor(ignore_index=lowerCAmelCase__		,  reduce_labels=lowerCAmelCase__)

			a__  :		Dict					=    image_processor(lowerCAmelCase__		,  return_tensors="""pt""")

			a__  :		Union[str, Any]					=    model(**lowerCAmelCase__)

			print("""Logits:"""		,  outputs.class_queries_logits[0, :3, :3])

			if model_name == "maskformer-swin-tiny-ade":
						a__  :		Dict					=    torch.tensor(
						    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]])
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,  lowerCAmelCase__		,  atol=1e-4)
			print("""Looks ok!""")

			if pytorch_dump_folder_path is not None:
						print(F'''Saving model and image processor to {pytorch_dump_folder_path}''')
						Path(lowerCAmelCase__).mkdir(exist_ok=lowerCAmelCase__)
						model.save_pretrained(lowerCAmelCase__)
						image_processor.save_pretrained(lowerCAmelCase__)

			if push_to_hub:
						print("""Pushing model and image processor to the hub...""")
						model.push_to_hub(F'''nielsr/{model_name}''')
						image_processor.push_to_hub(F'''nielsr/{model_name}''')


if __name__ == "__main__":
		_lowercase						: str     						=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default="maskformer-swin-tiny-ade",
		    type=str,
		    help=("Name of the MaskFormer model you\'d like to convert",),
		)
		parser.add_argument(
		    "--checkpoint_path",
		    default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
		    type=str,
		    help="Path to the original state dict (.pth file).",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
		)

		_lowercase						: int     						=parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






